The water and wastewater industry, often seen as slow to adopt new technologies, is on the cusp of a significant shift, thanks to the evolution of artificial intelligence (AI) and large language models (LLMs). While AI has been used for data analytics for years, the advent of generative AI (Gen AI) and LLMs is set to disrupt the sector, particularly for small utilities and those grappling with an aging workforce.
The primary disruptive force of Gen AI is its accessibility via human language, which fundamentally democratizes the use of this technology. “This means water utilities don’t need data scientists or software developers to use such tools,” says Christian Bonawandt. “Instead, employees can interact with data and make requests using normal human speech and text.” Moreover, the financial barrier to entry is low, with subscriptions to tools like Microsoft Copilot costing as little as $30 a month per person.
The industry is grappling with the “silver tsunami” — the loss of expertise due to retiring operators. AI tools offer a unique path forward, potentially accelerating digital transformation and even leapfrogging more widely adopted solutions. For instance, the Water Research Foundation (WRF) recently created a chatbot to capture the extensive skills of a long-retired operator, Jerry Kemp. Titled “What Would Jerry Do?: Chlorine,” the project trained the AI on hours of interviews with Kemp, an 82-year-old operator with more than four decades of experience. The result is a tool that operators can easily pose questions to and get detailed answers that include a “pro tip from Jerry” in his own words.
Another example is DC Water, which implemented an internal training tool that organizes videos of experts describing their procedures and then generates quizzes to test for comprehension as well as create searchable content. This application not only preserves institutional knowledge but also makes it readily accessible to new employees.
LLMs also excel at processing traditionally inaccessible unstructured data, including images, audio, video, PDF files, and handwritten notes. “LLMs use optical character recognition (OCR) to convert handwritten notes and daily logs into digitized output,” explains Bonawandt. “The AI can then structure the data into an Excel spreadsheet or similar format. From there, users can leverage the human language interface to request specific outcomes, such as data cleansing, correlation with other data sets, and more.”
However, the rapid advancement of AI also presents challenges. Data governance is critical, especially given the ability of LLMs to make previously inaccessible data processable. Clear rules on how data is managed, accessed, and used are essential. Training is also crucial for effective adoption. DC Water, for example, provided its employees with 12 weeks of training on the use of AI tools.
To secure funding for enterprise-grade LLMs, utilities must focus on fostering a community of shared learning and securing buy-in from leadership. The rapid rate of change means utilities cannot afford to sit back. Instead, success lies in taking a proactive approach that will enable them to stay ahead of the curve as AI continues to evolve.
This news could significantly shape the development of the water and sanitation sector. The democratization of AI tools could level the playing field, allowing smaller utilities to compete with larger ones. The preservation of institutional knowledge through AI could mitigate the impact of the “silver tsunami,” ensuring that critical expertise is not lost. Moreover, the ability of AI to process unstructured data could unlock valuable insights, improving operational efficiency and decision-making.
However, the sector must also grapple with the challenges posed by AI, including data governance and the need for comprehensive training. The proactive adoption of AI tools could position utilities to harness the full potential of this transformative technology, driving innovation and growth in the water and wastewater industry.